An Efficient Privacy-Preserving Ranked Keyword Search Method

Abstract

This research proposes a novel Privacy-Preserving Ranked Keyword Search (PPRKS) method leveraging secure multi-party computation (MPC) to enable efficient and privacy-preserving keyword searches over encrypted data. The method addresses the limitations of existing approaches by minimizing communication overhead and computational complexity while maintaining strong privacy guarantees. Our approach achieves ranked results without revealing the query or the underlying data to any single party, ensuring both data confidentiality and query privacy. Experimental results demonstrate significant improvements in efficiency compared to state-of-the-art techniques.

Introduction

Ranked keyword search (RKS) is crucial for efficient information retrieval. However, in scenarios involving sensitive data, such as medical records or financial transactions, the need for privacy-preserving RKS is paramount. Existing solutions often compromise either efficiency or privacy. The challenge lies in designing a system that allows users to search encrypted data, retrieve ranked results according to relevance, and prevent unauthorized access to the data or the search query itself. Current methods often suffer from high computational cost and communication overhead, limiting their scalability and practicality.

Objectives

• Develop an efficient privacy-preserving ranked keyword search algorithm.
• Minimize communication overhead and computational cost compared to existing methods.
• Guarantee strong privacy for both the data and the search query.